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Ruslan Prijadi
Faculty of Economics and Business, Universitas Indonesia, Salemba, Jakarta Pusat, 10440, Indonesia
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

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Risk Analysis of Operational Disruptions in Public Electric Vehicle Charging Stations Using the Failure Mode and Effects Analysis (FMEA) Method Teddy Maulana Putra; Ruslan Prijadi
Quantitative Economics and Management Studies Vol. 5 No. 3 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems2585

Abstract

The Indonesia government is actively promoting the adoption of electric vehicles, as detailed in the 2021-2030 Electricity Supply Business Plan. The state-owned electricity provider, PLN, is responsible for establishing Public Electric Vehicle Charging Stations (PEVCS). However, several of these stations have encountered malfunctions; notably, 82 of the 567 stations are classified as Unavailable, indicating they are non-functional. Research literature points to a financial loss of $34,000 from operational issues at PEVCSs. This research aims to helps management understand and prioritize disruption that leads to failures or damages at these stations. Method used is the Failure Mode and Effects Analysis (FMEA) method along with logistic regression to examine the disruptions at PEVCSs labeled as Unavailable. The data for this research comes from a six-month historical record of PEVCS disruptions. The variables utilized for logistic regression analysis include foundational variables from the FMEA methodology—Severity, Occurrence, and Disturbance—complemented by two supplementary variables: the speed and age of the PEVCS. Result was found that three out of twelve types of disruptions have a high likelihood of failure, specifically issues with Device Communication, Connectivity, and Emergency Stop functions. A disruption is deemed likely to cause failure if its probability exceeds 50%.
FMEA-Based Logistic Regression Model for the Evaluation of Photovoltaic Power Plant Risk Dianita Fitriani Program; Ruslan Prijadi
Quantitative Economics and Management Studies Vol. 5 No. 3 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems2645

Abstract

The purpose of this research is to identify the primary operational risks associated with photovoltaic power plants and develop effective risk management strategies to optimize the operation of existing plants and mitigate risks for future plants that will be constructed as part of the new renewable energy (EBT) transition agenda until 2030. The integration of Failure Mode and Effect Method Analysis (FMEA) with logistic regression provides the formation of a risk treatment ranking that management should prioritize. Risk assessment relies on the expertise and experience of professionals in performing their responsibilities associated with photovoltaic power plants. The research findings have identified 10 potential risks associated with improving photovoltaic power plants operations to prevent failure or damage to the system. These risks are categorized into five stages of the operation process: planning and procurement, installation, operation, and maintenance. Risk rankings and mitigation are generated to prioritize actions aimed at limiting the occurrence of failure/damage and low-capacity factors in photovoltaic power plants as recommendations for the management.
Co-Authors Dianita Fitriani Program Teddy Maulana Putra